Package 'mixR' reference manual

Title:	Finite Mixture Modeling for Raw and Binned Data
Description:	Performs maximum likelihood estimation for finite mixture models for families including Normal, Weibull, Gamma and Lognormal by using EM algorithm, together with Newton-Raphson algorithm or bisection method when necessary. It also conducts mixture model selection by using information criteria or bootstrap likelihood ratio test. The data used for mixture model fitting can be raw data or binned data. The model fitting process is accelerated by using R package 'Rcpp'.
Authors:	Youjiao Yu [aut, cre]
Maintainer:	Youjiao Yu <[email protected]>
License:	GPL (>= 2)
Version:	0.2.1
Built:	2024-10-21 05:23:04 UTC
Source:	CRAN

Finite Mixture Modeling for Raw and Binned Data

Description

The package mixR performs maximum likelihood estimation for finite mixture models for families including Normal, Weibull, Gamma and Lognormal via EM algorithm. It also conducts model selection by using information criteria or bootstrap likelihood ratio test. The data used for mixture model fitting can be raw data or binned data. The model fitting is accelerated by using R package Rcpp.

Details

Finite mixture models can be represented by

$f(x; \Phi) = \sum_{j = 1}^g \pi_j f_j(x; \theta_j)$

where $f(x; \Phi)$ is the probability density function (p.d.f.) or probability mass function (p.m.f.) of the mixture model, $f_j(x; \theta_j)$ is the p.d.f. or p.m.f. of the $j$ th component of the mixture model, $\pi_j$ is the proportion of the $j$ th component and $\theta_j$ is the parameter of the $j$ th component, which can be a scalar or a vector, $\Phi$ is a vector of all the parameters of the mixture model. The maximum likelihood estimate of the parameter vector $\Phi$ can be obtained by using the EM algorithm (Dempster et al, 1977). The binned data is present sometimes instead of the raw data, for the reason of storage convenience or necessity. The binned data is recorded in the form of $(a_i, b_i, n_i)$ where $a_i$ is the lower bound of the $i$ th bin, $b_i$ is the upper bound of the $i$ th bin, and $n_i$ is the number of observations that fall in the $i$ th bin, for $i = 1, \dots, r$ , and $r$ is the total number of bins.

To obtain maximum likelihood estimate of the finite mixture model for binned data, we can introduce two types of latent variables $x$ and $z$ , where $x$ represents the value of the unknown raw data, and $z$ is a vector of zeros and one indicating the component that $x$ belongs to. To use the EM algorithm we first write the complete-data log-likelihood

$Q(\Phi; \Phi^{(p)}) = \sum_{j = 1}^{g} \sum_{i = 1}^r n_i z^{(p)} [\log f(x^{(p)}; \theta_j) + \log \pi_j ]$

where $z^{(p)}$ is the expected value of $z$ given the estimated value of $\Phi$ and expected value $x^{(p)}$ at $p$ th iteration. The estimated value of $\Phi$ can be updated iteratively via the E-step, in which we estimate $\Phi$ by maximizing the complete-data loglikelihood, and M-step, in which we calculate the expected value of the latent variables $x$ and $z$ . The EM algorithm is terminated by using a stopping rule. The M-step of the EM algorithm may or may not have closed-form solution (e.g. the Weibull mixture model or Gamma mixture model). If not, an iterative approach like Newton's algorithm or bisection method may be used.

For a given data set, when we have no prior information about the number of components $g$ , its value should be estimated from the data. Because mixture models don't satisfy the regularity condition for the likelihood ratio test (which requires that the true parameter under the null hypothesis should be in the interior of the parameter space of the full model under the alternative hypothesis), a bootstrap approach is usually used in the literature (see McLachlan (1987, 2004), Feng and McCulloch (1996)). The general step of bootstrap likelihood ratio test is as follows.

For the given data $x$ , estimate $\Phi$ under both the null and the alternative hypothesis to get $\hat\Phi_0$ and $\hat\Phi_1$ . Calculate the observed log-likelihood $\ell(x; \hat\Phi_0)$ and $\ell(x; \hat\Phi_1)$ . The likelihood ratio test statistic is defined as

$w_0 = -2(\ell(x; \hat\Phi_0) - \ell(x; \hat\Phi_1)).$
Generate random data of the same size as the original data $x$ from the model under the null hypothesis using estimated parameter $\hat\Phi_0$ , then repeat step 1 using the simulated data. Repeat this process for $B$ times to get a vector of the simulated likelihood ratio test statistics $w_1^{1}, \dots, w_1^{B}$ .
Calculate the empirical p-value

$p = \frac{1}{B} \sum_{i=1}^B I(w_1^{(i)} > w_0)$

where $I$ is the indicator function.

This package does the following three things.

Fitting finite mixture models for both raw data and binned data by using EM algorithm, together with Newton-Raphson algorithm and bisection method.
Do parametric bootstrap likelihood ratio test for two candidate models.
Do model selection by Bayesian information criterion.

To speed up computation, the EM algorithm is fulfilled in C++ by using Rcpp (Eddelbuettel and Francois (2011)).

Author(s)

Maintainer: Youjiao Yu [email protected]

References

Dempster, A. P., Laird, N. M., and Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm. Journal of the royal statistical society. Series B (methodological), pages 1-38, 1977.

Dirk Eddelbuettel and Romain Francois (2011). Rcpp: Seamless R and C++ Integration. Journal of Statistical Software, 40(8), 1-18. URL http://www.jstatsoft.org/v40/i08/.

Efron, B. Bootstrap methods: Another look at the jackknife. Ann. Statist., 7(1):1-26, 01 1979.

Feng, Z. D. and McCulloch, C. E. Using bootstrap likelihood ratios in finite mixture models. Journal of the Royal Statistical Society. Series B (Methodological), pages 609-617, 1996.

Lo, Y., Mendell, N. R., and Rubin, D. B. Testing the number of components in a normal mixture. Biometrika, 88(3):767-778, 2001.

McLachlan, G. J. On bootstrapping the likelihood ratio test statistic for the number of components in a normal mixture. Applied statistics, pages 318-324, 1987.

McLachlan, G. and Jones, P. Fitting mixture models to grouped and truncated data via the EM algorithm. Biometrics, pages 571-578, 1988.

McLachlan, G. and Peel, D. Finite mixture models. John Wiley & Sons, 2004.

Binning the Raw Data

Description

This function creates a binned data set from the raw data

Usage

bin(x, brks)
bin(x, brks)

Arguments

`x`	a numeric vector of raw data
`brks`	a numeric vector in increasing order, representing the bin values within each of which we want to calculate the frequency of the data

Details

Given a numeric vector, the function bin creates a binned data set with bin values provided by brks. Fitting mixture models with a large data set may be slow, especially when we want to fit non-normal mixture models. Binning the data with a relatively small bin width speeds up the computation of EM algorithm while at the same time keeps the precision of the estimation result.

Value

The function bin returns a matrix with three columns, representing the value of the left bin, the value of the right bin and the number of observations in x that falls in each bin.

Examples

set.seed(99)
x <- rmixnormal(200, c(0.5, 0.5), c(2, 5), c(1, 1))
data <- bin(x, seq(-2, 10, 0.1))
fit1 <- mixfit(x, ncomp = 2)
fit2 <- mixfit(data, ncomp = 2)

set.seed(99)
x <- rmixnormal(200, c(0.5, 0.5), c(2, 5), c(1, 1))
data <- bin(x, seq(-2, 10, 0.1))
fit1 <- mixfit(x, ncomp = 2)
fit2 <- mixfit(data, ncomp = 2)

Bootstrap Likelihood Ratio Test for Finite Mixture Models

Description

This function performs the likelihood ratio test by parametric bootstrapping for two mixture models with different number of components.

Usage

bs.test(
  x,
  ncomp = c(1, 2),
  family = c("normal", "weibull", "gamma", "lnorm"),
  B = 100,
  ev = FALSE,
  mstep.method = c("bisection", "newton"),
  init.method = c("kmeans", "hclust"),
  tol = 1e-06,
  max_iter = 500
)
bs.test(
  x,
  ncomp = c(1, 2),
  family = c("normal", "weibull", "gamma", "lnorm"),
  B = 100,
  ev = FALSE,
  mstep.method = c("bisection", "newton"),
  init.method = c("kmeans", "hclust"),
  tol = 1e-06,
  max_iter = 500
)

Arguments

`x`	a numeric vector for the raw data or a three-column matrix for the binned data.
`ncomp`	a vector of two positive integers specifying the number of components of the mixture model under the null and alternative hypothesis. The first integer should be smaller than the second one. The default value is `c(1, 2)`.
`family`	a character string specifying the family of the mixture model, which can be one of `normal`, `weibull`, `gamma`, or `lnorm` (default `normal`).
`B`	the number of bootstrap iterations (default 100).
`ev`	a logical value indicating whether the variance of each component should be the same or not (default `FALSE` for `Normal` family and ignored for other family members).
`mstep.method`	the method used in M-step of EM algorithm for `weibull` or `gamma` family. It is ignored for `normal` or `lnorm` family, which has closed-form solution in the M-step. The default value is `bisection`.
`init.method`	a character string specifying the method used for providing the initial values for the parameters for the EM algorithm. It can be one of `kmeans` or `hclust`. The default is `kmeans`
`tol`	the tolerance for the stopping rule of EM algorithm. It is the value to stop EM algorithm when the two consecutive iterations produces log-likelihood with difference less than `tol`. The default value is 1e-6.
`max_iter`	the maximum number of iterations for the EM algorithm (default 500).

Details

For the given data x and the specified family, the function bs.test conducts a bootstrap likelihood ratio test for two mixture models with the number of components under the null and the alternative hypothesis specified in ncomp.

Value

The function bs.test returns an object of class bootEM which contains the following three items.

`pvalue`	The p-value of the bootstrap likelihood ratio test
`w0`	the observed likelihood ratio test statistic
`w1`	a vector of simulated likelihood ratio test statistics

Examples

## testing normal mixture models with 2 and 3 components
set.seed(100)
x <- rmixnormal(200, c(0.5, 0.5), c(2, 5), c(1, 0.7))
ret <- bs.test(x, ncomp = c(2, 3), B = 30)
ret

## (not run) testing Weibull mixture models with 2 and 3 components
## set.seed(101)
## x <- rmixweibull(200, c(0.3, 0.4, 0.3), c(2, 5, 8), c(1, 0.6, 0.8))
## ret <- bs.test(x, ncomp = c(2, 3), family = "weibull", B = 30)
## ret

## (not run) testing Gamma mixture models with 1 and 2 components
## set.seed(102)
## x <- rgamma(200, 2, 1)
## ret <- bs.test(x, ncomp = c(1, 2), family = "gamma", B = 30)
## ret

## testing normal mixture models with 2 and 3 components
set.seed(100)
x <- rmixnormal(200, c(0.5, 0.5), c(2, 5), c(1, 0.7))
ret <- bs.test(x, ncomp = c(2, 3), B = 30)
ret

## (not run) testing Weibull mixture models with 2 and 3 components
## set.seed(101)
## x <- rmixweibull(200, c(0.3, 0.4, 0.3), c(2, 5, 8), c(1, 0.6, 0.8))
## ret <- bs.test(x, ncomp = c(2, 3), family = "weibull", B = 30)
## ret

## (not run) testing Gamma mixture models with 1 and 2 components
## set.seed(102)
## x <- rgamma(200, 2, 1)
## ret <- bs.test(x, ncomp = c(1, 2), family = "gamma", B = 30)
## ret

The Density of Finite Mixture Models

Description

This function calculates the probability density of a finite mixture model.

Usage

## S3 method for class 'mixfitEM'
density(x, at, smoothness = 512, cut = 3.8, ...)
## S3 method for class 'mixfitEM'
density(x, at, smoothness = 512, cut = 3.8, ...)

Arguments

`x`	an object of class `mixfitEM`
`at`	a scalar or a numeric vector of locations where densities are calculated
`smoothness`	a positive integer controlling the smoothness of the density curve (default 512). The higher this value is, the more locations of the mixture model the density is calculated.
`cut`	the number of standard deviations away the density is to be computed (default 3.8)
`...`	other arguments

Details

The function density.mixfitEM is the method of the generic function density for the class mixfitEM.

Value

This function returns a list of class densityEM, which contains the following items.

`x`	a scalar or a numeric vector of locations where densities are calculated.
`y`	a vector of the densities of the mixture model at the corresponding locations in `x`
`comp`	a matrix with columns representing the densities of each component in the mixture model at the corresponding locations in `x`

Examples

set.seed(102)
x <- rmixnormal(200, c(0.5, 0.5), c(2, 5), c(1, 0.7))
fit1 <- mixfit(x, ncomp = 2)
d1 = density(fit1)
d2 = density(fit1, at = 0)

set.seed(102)
x <- rmixnormal(200, c(0.5, 0.5), c(2, 5), c(1, 0.7))
fit1 <- mixfit(x, ncomp = 2)
d1 = density(fit1)
d2 = density(fit1, at = 0)

Initialization of the EM Algorithm

Description

This function returns the mean and standard deviation of each component by using K-means clustering or hierarchical clustering.

Usage

initz(x, ncomp, init.method = c("kmeans", "hclust"))
initz(x, ncomp, init.method = c("kmeans", "hclust"))

Arguments

`x`	a numeric vector of the raw data or a three-column matrix of the binned data
`ncomp`	a positive integer specifying the number of components for a mixture model
`init.method`	the method used for providing initial values, which can be one of `kmeans` or `hclust`.

Details

The function initz returns the mean and standard deviation of each component of a mixture model by using K-means clustering algorithm, or hierarchical clustering method. It is used for automatically selecting initial values for the EM algorithm, so as to enable mixture model selection by bootstrapping likelihood ratio test or using information criteria.

Value

initz returns a list with three items

`pi`	a numeric vector of component proportions
`mu`	a numeric vector of component means
`sd`	a numeric vector of component standard deviations

Examples

x <- rmixnormal(500, c(0.5, 0.5), c(2, 5), c(1, 0.7))
data <- bin(x, seq(-2, 8, 0.25))
par1 <- initz(x, 2)
par2 <- initz(data, 2)

x <- rmixnormal(500, c(0.5, 0.5), c(2, 5), c(1, 0.7))
data <- bin(x, seq(-2, 8, 0.25))
par1 <- initz(x, 2)
par2 <- initz(data, 2)

Finite Mixture Modeling for Raw Data and Binned Data

Description

This function is used to perform the maximum likelihood estimation for a variety of finite mixture models for both raw and binned data by using the EM algorithm, together with Newton-Raphson algorithm or bisection method when necessary.

Usage

mixfit(
  x,
  ncomp = NULL,
  family = c("normal", "weibull", "gamma", "lnorm"),
  pi = NULL,
  mu = NULL,
  sd = NULL,
  ev = FALSE,
  mstep.method = c("bisection", "newton"),
  init.method = c("kmeans", "hclust"),
  tol = 1e-06,
  max_iter = 500
)
mixfit(
  x,
  ncomp = NULL,
  family = c("normal", "weibull", "gamma", "lnorm"),
  pi = NULL,
  mu = NULL,
  sd = NULL,
  ev = FALSE,
  mstep.method = c("bisection", "newton"),
  init.method = c("kmeans", "hclust"),
  tol = 1e-06,
  max_iter = 500
)

Arguments

`x`	a numeric vector for the raw data or a three-column matrix for the binned data
`ncomp`	a positive integer specifying the number of components of the mixture model
`family`	a character string specifying the family of the mixture model. It can only be one element from `normal`, `weibull`, `gamma` or `lnorm`.
`pi`	a vector of the initial value for the proportion
`mu`	a vector of the initial value for the mean
`sd`	a vector of the initial value for the standard deviation
`ev`	a logical value controlling whether each component has the same variance when fitting normal mixture models. It is ignored when fitting other mixture models. The default is `FALSE`.
`mstep.method`	a character string specifying the method used in M-step of the EM algorithm when fitting weibull or gamma mixture models. It can be either `bisection` or `newton`. The default is `bisection`.
`init.method`	a character string specifying the method used for providing initial values for the parameters for EM algorithm. It can be one of `kmeans` or `hclust`. The default is `kmeans`
`tol`	the tolerance for the stopping rule of EM algorithm. It is the value to stop EM algorithm when the two consecutive iterations produces loglikelihood with difference less than `tol`. The default value is 1e-6.
`max_iter`	the maximum number of iterations for the EM algorithm (default 500).

Details

The function mixfit is the core function in this package. It is used to perform the maximum likelihood estimation for finite mixture models from the families of normal, weibull, gamma or lognormal by using the EM algorithm. When the family is weibull or gamma, the M-step of the EM algorithm has no closed-form solution and we can use Newton algorithm by specifying method = "newton" or use bisection method by specifying method = "bisection".

The initial values of the EM algorithm can be provided by specifying the proportion of each component pi, the mean of each component mu and the standard deviation of each component sd. If one or more of these initial values are not provided, then their values are estimated by using K-means clustering method or hierarchical clustering method. If all of pi, mu, and sd are not provided, then ncomp should be provided so initial values are automatically generated. For the normal mixture models, we can control whether each component has the same variance or not.

Value

the function mixfit return an object of class mixfitEM, which contains a list of different number of items when fitting different mixture models. The common items include

`pi`	a numeric vector representing the estimated proportion of each component
`mu`	a numeric vector representing the estimated mean of each component
`sd`	a numeric vector representing the estimated standard deviation of each component
`iter`	a positive integer recording the number of EM iteration performed
`loglik`	the loglikelihood of the estimated mixture model for the data `x`
`aic`	the value of AIC of the estimated model for the data `x`
`bic`	the value of BIC of the estimated model for the data `x`
`data`	the data `x`
`comp.prob`	the probability that `x` belongs to each component
`family`	the family the mixture model belongs to

For the Weibull mixture model, the following extra items are returned.

`k`	a numeric vector representing the estimated shape parameter of each component
`lambda`	a numeric vector representing the estimated scale parameter of each component

For the Gamma mixture model, the following extra items are returned.

`alpha`	a numeric vector representing the estimated shape parameter of each component
`lambda`	a numeric vector representing the estimated rate parameter of each component

For the lognormal mixture model, the following extra items are returned.

`mulog`	a numeric vector representing the estimated logarithm mean of each component
`sdlog`	a numeric vector representing the estimated logarithm standard deviation of each component

Examples

## fitting the normal mixture models
set.seed(103)
x <- rmixnormal(200, c(0.3, 0.7), c(2, 5), c(1, 1))
data <- bin(x, seq(-1, 8, 0.25))
fit1 <- mixfit(x, ncomp = 2)  # raw data
fit2 <- mixfit(data, ncomp = 2)  # binned data
fit3 <- mixfit(x, pi = c(0.5, 0.5), mu = c(1, 4), sd = c(1, 1))  # providing the initial values
fit4 <- mixfit(x, ncomp = 2, ev = TRUE)  # setting the same variance

## (not run) fitting the weibull mixture models
## x <- rmixweibull(200, c(0.3, 0.7), c(2, 5), c(1, 1))
## data <- bin(x, seq(0, 8, 0.25))
## fit5 <- mixfit(x, ncomp = 2, family = "weibull")  # raw data
## fit6 <- mixfit(data, ncomp = 2, family = "weibull")  # binned data

## (not run) fitting the Gamma mixture models
## x <- rmixgamma(200, c(0.3, 0.7), c(2, 5), c(1, 1))
## data <- bin(x, seq(0, 8, 0.25))
## fit7 <- mixfit(x, ncomp = 2, family = "gamma")  # raw data
## fit8 <- mixfit(data, ncomp = 2, family = "gamma")  # binned data

## (not run) fitting the lognormal mixture models
## x <- rmixlnorm(200, c(0.3, 0.7), c(2, 5), c(1, 1))
## data <- bin(x, seq(0, 8, 0.25))
## fit9 <- mixfit(x, ncomp = 2, family = "lnorm")  # raw data
## fit10 <- mixfit(data, ncomp = 2, family = "lnorm")  # binned data

## fitting the normal mixture models
set.seed(103)
x <- rmixnormal(200, c(0.3, 0.7), c(2, 5), c(1, 1))
data <- bin(x, seq(-1, 8, 0.25))
fit1 <- mixfit(x, ncomp = 2)  # raw data
fit2 <- mixfit(data, ncomp = 2)  # binned data
fit3 <- mixfit(x, pi = c(0.5, 0.5), mu = c(1, 4), sd = c(1, 1))  # providing the initial values
fit4 <- mixfit(x, ncomp = 2, ev = TRUE)  # setting the same variance

## (not run) fitting the weibull mixture models
## x <- rmixweibull(200, c(0.3, 0.7), c(2, 5), c(1, 1))
## data <- bin(x, seq(0, 8, 0.25))
## fit5 <- mixfit(x, ncomp = 2, family = "weibull")  # raw data
## fit6 <- mixfit(data, ncomp = 2, family = "weibull")  # binned data

## (not run) fitting the Gamma mixture models
## x <- rmixgamma(200, c(0.3, 0.7), c(2, 5), c(1, 1))
## data <- bin(x, seq(0, 8, 0.25))
## fit7 <- mixfit(x, ncomp = 2, family = "gamma")  # raw data
## fit8 <- mixfit(data, ncomp = 2, family = "gamma")  # binned data

## (not run) fitting the lognormal mixture models
## x <- rmixlnorm(200, c(0.3, 0.7), c(2, 5), c(1, 1))
## data <- bin(x, seq(0, 8, 0.25))
## fit9 <- mixfit(x, ncomp = 2, family = "lnorm")  # raw data
## fit10 <- mixfit(data, ncomp = 2, family = "lnorm")  # binned data

Plot Bootstrap Likelihood Ratio Test

Description

This function is the plot method for the class bootEM.

Usage

## S3 method for class 'bootEM'
plot(x, ...)
## S3 method for class 'bootEM'
plot(x, ...)

Arguments

`x`	an object of class `bootEM`, which is the output of the function `bs.test`.
`...`	the other parameters passed to the function `hist`

Details

The histogram of the bootstrap LRT statistics $w_1$ is plotted, with the observed LRT statistic imposed in a red vertical line.

Examples

## plotting the bootstrap LRT result
set.seed(100)
x <- rmixnormal(200, c(0.5, 0.5), c(2, 5), c(1, 0.7))
ret <- bs.test(x, ncomp = c(2, 3), B = 30)
plot(ret)

## plotting the bootstrap LRT result
set.seed(100)
x <- rmixnormal(200, c(0.5, 0.5), c(2, 5), c(1, 0.7))
ret <- bs.test(x, ncomp = c(2, 3), B = 30)
plot(ret)

Plotting the Fitted Mixture Models

Description

This is the plot method for the class mixfitEM. It is used to plot the fitted mixture models by using base R plotting system or using the package ggplot2.

Usage

## S3 method for class 'mixfitEM'
plot(
  x,
  theme = NULL,
  add_hist = TRUE,
  add_poly = TRUE,
  add_legend = TRUE,
  smoothness = 512,
  trans = 0.5,
  cut = 3.8,
  xlab,
  ylab,
  title,
  breaks,
  plot.title = element_text(hjust = 0.5),
  axis.text.x = element_text(),
  axis.text.y = element_text(),
  axis.title.x = element_text(),
  axis.title.y = element_text(),
  legend.title = element_text(),
  legend.text = element_text(),
  legend.position = "right",
  legend.direction = ifelse(legend.position %in% c("top", "bottom"), "horizontal",
    "vertical"),
  ...
)
## S3 method for class 'mixfitEM'
plot(
  x,
  theme = NULL,
  add_hist = TRUE,
  add_poly = TRUE,
  add_legend = TRUE,
  smoothness = 512,
  trans = 0.5,
  cut = 3.8,
  xlab,
  ylab,
  title,
  breaks,
  plot.title = element_text(hjust = 0.5),
  axis.text.x = element_text(),
  axis.text.y = element_text(),
  axis.title.x = element_text(),
  axis.title.y = element_text(),
  legend.title = element_text(),
  legend.text = element_text(),
  legend.position = "right",
  legend.direction = ifelse(legend.position %in% c("top", "bottom"), "horizontal",
    "vertical"),
  ...
)

Arguments

`x`	an object of class `mixfitEM`, an output from the function `mixfit`
`theme`	a string the specifies the appearance of the plot, which is from the ggplot2 and could be one of'gray', 'bw' (default), 'linedraw', 'light', 'dark', 'minimal', 'classic', or 'void'.
`add_hist`	a logical value specifying whether a histogram of data should be plotted
`add_poly`	a logical value specifying whether a polygon of each component should be plotted.
`add_legend`	a logical value specifying whether the legend should be plotted.
`smoothness`	a positive integer controlling the smoothness of the density curve in the plot. The default value is 512 and increasing this value will produce smoother curve.
`trans`	the transparency of the polygons if they are plotted (default 0.5)
`cut`	the number of standard deviations from the center of each component we want to plot the density (default 3.8)
`xlab`	the label for x axis
`ylab`	the label for y axis
`title`	the title of the plot
`breaks`	the number of bins used for plotting the histogram
`plot.title`	an object returned by element_text() to specify the appearance of the title
`axis.text.x`	an object returned by element_text() to specify the appearance of the x axis
`axis.text.y`	an object returned by element_text() to specify the appearance of the y axis
`axis.title.x`	an object returned by element_text() to specify the appearance of the label along x axis
`axis.title.y`	an object returned by element_text() to specify the appearance of the label along y axis
`legend.title`	an object returned by element_text() to specify the appearance of the legend title
`legend.text`	an object returned by element_text() to specify the appearance of the legend text
`legend.position`	the position of the legend, could be 'right'(default), 'right', 'top', or 'bottom'
`legend.direction`	the direction of the legend, could be 'vertical' (default) or 'horizontal'
`...`	other arguments

Details

The function plot.mixfitEM is used for plotting an object of class mixfitEM, which is an output of the function mixfit. Users can choose base R plotting system or ggplot2 (the package ggplot2 needs to be installed). plotting system. The plot is a density plot of the fitted mixture model imposed on top of a histogram. The parameters that control the appearance of the histogram and the density curve can be changed. The density curve of each component can be shown or hidden.

Examples

x <- rmixnormal(200, c(0.3, 0.7), c(2, 5), c(1, 0.7))
mod <- mixfit(x, ncomp = 2)
plot(mod)
plot(mod, theme = 'classic') 

x <- rmixnormal(200, c(0.3, 0.7), c(2, 5), c(1, 0.7))
mod <- mixfit(x, ncomp = 2)
plot(mod)
plot(mod, theme = 'classic')

Plot Method for Class `selectEM`

Description

This function plots the result of mixture model selection by BIC.

Usage

## S3 method for class 'selectEM'
plot(x, leg.loc = "topright", ...)
## S3 method for class 'selectEM'
plot(x, leg.loc = "topright", ...)

Arguments

`x`	an object of class `selectEM`, which is an output of the function `select`.
`leg.loc`	the location of the legend, which is the same as the first argument of the function
`...`	other arguments passed to `plot` `legend`. The default value is "topright". The user can change its location (to "topleft", "bottom right" etc.) if the visual plot conflicts with the legend.

Details

The function plot.selectEM is the plot method for the class selectEM. It plots the number of components against the corresponding value of BIC. It is used to visually display the mixture model selection result by BIC.

Examples

x <- rmixnormal(200, c(0.3, 0.7), c(2, 5), c(1, 1))
res <- select(x, ncomp = 1:3)
plot(res)

x <- rmixnormal(200, c(0.3, 0.7), c(2, 5), c(1, 1))
res <- select(x, ncomp = 1:3)
plot(res)

Print Method for Class `mixfitEM`

Description

This function is the print method for the mixfitEM class.

Usage

## S3 method for class 'mixfitEM'
print(x, digits = getOption("digits"), ...)
## S3 method for class 'mixfitEM'
print(x, digits = getOption("digits"), ...)

Arguments

`x`	an object of class `mixfitEM`
`digits`	the digits to print for the values in the print output. The default value is from the global option `getOption("digits")`.
`...`	other arguments passed to `print`

Details

print.mixfitEM prints the value of the parameters of a fitted mixture model, together with some other information like the number of iterations of the EM algorithm, the loglikelihood, the value of AIC and BIC.

Examples

x <- rmixnormal(200, c(0.5, 0.5), c(2, 5), c(1, 0.7))
fit <- mixfit(x, ncomp = 2)
print(x)

x <- rmixnormal(200, c(0.5, 0.5), c(2, 5), c(1, 0.7))
fit <- mixfit(x, ncomp = 2)
print(x)

Print Method for Class `selectEM`

Description

The function prints the result of mixture model selection.

Usage

## S3 method for class 'selectEM'
print(x, ...)
## S3 method for class 'selectEM'
print(x, ...)

Arguments

`x`	an object of class `selectEM`
`...`	other arguments passed to `print`

Details

The function print.selectEM is the print method for the class selectEM, which is the output of the function select. It prints a data frame which contains the following information of each candidate mixture models: the number of components, whether the variance is the same for each component in a mixture model (only for normal), the value of BIC, and an indicator of the best model.

Reinstate the Binned Data to the Raw Data

Description

This function creates a numeric vector approximating the raw data from binned data

Usage

reinstate(data)
reinstate(data)

Arguments

data

a three-column matrix representing the raw data

Details

The function reinstate creates a numeric vector by generating $n_i$ random data from the Uniform distribution $U(a_i, b_i)$ for $i = 1, \dots, r$ and then combine all random data together. $a_i, b_i, n_i$ are the first, second and the third column of the matrix data and $r$ is the number of bins. It is used for enabling parameter initialization for EM algorithm when we fit mixture models for binned data.

Value

The function returns a numeric vector.

Examples

x <- rnorm(100)
data <- bin(x, seq(-3, 3, 0.25))
y <- reinstate(data)

x <- rnorm(100)
data <- bin(x, seq(-3, 3, 0.25))
y <- reinstate(data)

Generating Random Data From A Gamma Mixture Model

Description

The function rmixgamma generates random data from a Gamma mixture model.

Usage

rmixgamma(n, pi, mu, sd)
rmixgamma(n, pi, mu, sd)

Arguments

`n`	a positive integer specifying the number of observations we want to generate from the mixture model
`pi`	a numeric vector for the proportion of each component
`mu`	a numeric vector for the mean of each component
`sd`	a numeric vector for the standard deviation of each component

Details

The number of random data from each component $n_0$ (a vector) is generated from a multinomial distribution Multinom $(n, pi)$ . Then the random data from each component is generated with the sample sized specified in $n_0$ and parameters of Gamma distributions specified in mu and sd.

Value

The function rmixgamma returns a numeric vector of random data from the specified Gamma mixture model.

Examples

x <- rmixgamma(1000, c(0.4, 0.6), c(2, 5), c(1, 0.5))
hist(x, breaks = 40)

x <- rmixgamma(1000, c(0.4, 0.6), c(2, 5), c(1, 0.5))
hist(x, breaks = 40)

Generating Random Data From A Lognormal Mixture Model

Description

The function rmixlnorm generates random data from a lognormal mixture model.

Usage

rmixlnorm(n, pi, mu, sd)
rmixlnorm(n, pi, mu, sd)

Arguments

`n`	a positive integer specifying the number of observations we want to generate from the mixture model
`pi`	a numeric vector for the proportion of each component
`mu`	a numeric vector for the mean of each component
`sd`	a numeric vector for the standard deviation of each component

Details

Value

The function rmixlnorm returns a numeric vector of random data from the specified lognormal mixture model.

Examples

x <- rmixlnorm(1000, c(0.4, 0.6), c(2, 5), c(1, 0.5))
hist(x, breaks = 40)

x <- rmixlnorm(1000, c(0.4, 0.6), c(2, 5), c(1, 0.5))
hist(x, breaks = 40)

Generating Random Data From A Normal Mixture Model

Description

The function rmixnormal generates random data from a normal mixture model.

Usage

rmixnormal(n, pi, mu, sd)
rmixnormal(n, pi, mu, sd)

Arguments

`n`	a positive integer specifying the number of observations we want to generate from the mixture model
`pi`	a numeric vector for the proportion of each component
`mu`	a numeric vector for the mean of each component
`sd`	a numeric vector for the standard deviation of each component

Details

Value

The function rmixnormal returns a numeric vector of random data from the specified normal mixture model.

Examples

x <- rmixnormal(1000, c(0.4, 0.6), c(2, 5), c(1, 0.5))
hist(x, breaks = 40)

x <- rmixnormal(1000, c(0.4, 0.6), c(2, 5), c(1, 0.5))
hist(x, breaks = 40)

Generating Random Data From A Weibull Mixture Model

Description

The function rmixweibull generates random data from a normal Weibull model.

Usage

rmixweibull(n, pi, mu, sd)
rmixweibull(n, pi, mu, sd)

Arguments

`n`	a positive integer specifying the number of observations we want to generate from the mixture model
`pi`	a numeric vector for the proportion of each component
`mu`	a numeric vector for the mean of each component
`sd`	a numeric vector for the standard deviation of each component

Details

Value

The function rmixweibull returns a numeric vector of random data from the specified Weibull mixture model.

Examples

x <- rmixweibull(1000, c(0.4, 0.6), c(2, 5), c(1, 0.5))
hist(x, breaks = 40)

x <- rmixweibull(1000, c(0.4, 0.6), c(2, 5), c(1, 0.5))
hist(x, breaks = 40)

Finite Mixture Model Selection by Information Criterion

Description

This function selects the best model from a candidate of mixture models based on the information criterion BIC.

Usage

select(
  x,
  ncomp,
  family = c("normal", "weibull", "gamma", "lnorm"),
  mstep.method = c("bisection", "newton"),
  init.method = c("kmeans", "hclust"),
  tol = 1e-06,
  max_iter = 500
)
select(
  x,
  ncomp,
  family = c("normal", "weibull", "gamma", "lnorm"),
  mstep.method = c("bisection", "newton"),
  init.method = c("kmeans", "hclust"),
  tol = 1e-06,
  max_iter = 500
)

Arguments

`x`	a numeric vector for raw data or a three-column matrix for the binned data
`ncomp`	a vector of positive integers specifying the number of components of the candidate mixture models
`family`	a character string specifying the family of the mixture model. It can only be one element from `normal`, `weibull`, `gamma` or `lnorm`.
`mstep.method`	a character string specifying the method used in M-step of the EM algorithm when fitting weibull or gamma mixture models. It can be either `bisection` or `newton`. The default is `bisection`.
`init.method`	a character string specifying the method used for providing initial values for the parameters for EM algorithm. It can be one of `kmeans` or `hclust`. The default is `kmeans`
`tol`	the tolerance for the stopping rule of EM algorithm. It is the value to stop EM algorithm when the two consecutive iterations produces loglikelihood with difference less than `tol`. The default value is 1e-6.
`max_iter`	the maximum number of iterations for the EM algorithm (default 500).

Details

By specifying different number of components, the function select fits a series of mixture models for a given family, and a mixture model with minimum value of BIC is regarded as the best.

Value

The function returns an object of class selectEM which contains the following items.

`ncomp`	the specified number of components of the candidate mixture models
`equal.var`	a logical vector indicating whether the variances of each component in each mixture model are constrained to be the same (only for `normal` family)
`bic`	the value of BIC for each mixture model
`best`	an indicator of the best model
`family`	the family of the mixture model

Examples

## selecting the optimal normal mixture model by BIC
set.seed(105)
x <- rmixnormal(1000, c(0.3, 0.4, 0.3), c(-4, 0, 4), c(1, 1, 1))
hist(x, breaks = 40)
ret <- select(x, ncomp = 2:5)
## [1] "The final model: normal mixture (equal variance) with 3 components"

## (not run) selecting the optimal Weibull mixture model by BIC
## set.seed(106)
## x <- rmixweibull(1000, c(0.3, 0.4, 0.3), c(2, 5, 8), c(0.7, 0.6, 1))
## ret <- select(x, ncomp = 2:5, family = "weibull")
## [1] "The final model: weibull mixture with 3 components"

## (not run) selecting the optimal Gamma mixture model by BIC
## set.seed(107)
## x <- rmixgamma(1000, c(0.3, 0.7), c(2, 5), c(0.7, 1))
## ret <- select(x, ncomp = 2:5, family = "gamma")
## [1] "The final model: gamma mixture with 2 components"


## (not run) selecting the optimal lognormal mixture model by BIC
## set.seed(108)
## x <- rmixlnorm(1000, c(0.2, 0.3, 0.2, 0.3), c(4, 7, 9, 12), c(1, 0.5, 0.7, 1))
## ret <- select(x, ncomp = 2:6, family = "lnorm")
## [1] "The final model: lnorm mixture with 4 components"

## selecting the optimal normal mixture model by BIC
set.seed(105)
x <- rmixnormal(1000, c(0.3, 0.4, 0.3), c(-4, 0, 4), c(1, 1, 1))
hist(x, breaks = 40)
ret <- select(x, ncomp = 2:5)
## [1] "The final model: normal mixture (equal variance) with 3 components"

## (not run) selecting the optimal Weibull mixture model by BIC
## set.seed(106)
## x <- rmixweibull(1000, c(0.3, 0.4, 0.3), c(2, 5, 8), c(0.7, 0.6, 1))
## ret <- select(x, ncomp = 2:5, family = "weibull")
## [1] "The final model: weibull mixture with 3 components"

## (not run) selecting the optimal Gamma mixture model by BIC
## set.seed(107)
## x <- rmixgamma(1000, c(0.3, 0.7), c(2, 5), c(0.7, 1))
## ret <- select(x, ncomp = 2:5, family = "gamma")
## [1] "The final model: gamma mixture with 2 components"


## (not run) selecting the optimal lognormal mixture model by BIC
## set.seed(108)
## x <- rmixlnorm(1000, c(0.2, 0.3, 0.2, 0.3), c(4, 7, 9, 12), c(1, 0.5, 0.7, 1))
## ret <- select(x, ncomp = 2:6, family = "lnorm")
## [1] "The final model: lnorm mixture with 4 components"

1872 Hidalgo Stamp Data

Description

A vector containing the 1872 Hidalgo stamp data

Usage

Stamp
Stamp

Format

A vector with 485 measurements of the thickness (nm) of the stamps

References

Izenman, A. J. and Sommer, C. J. Philatelic mixtures and multimodal densities. Journal of the American Statistical association, 83(404):941-953, 1988.

1872 Hidalgo Stamp Data (Binned)

Description

A dataset containing the 1872 Hidalgo stamp data in the form of binned data

Usage

Stamp2
Stamp2

Format

A matrix with 62 rows and 3 columns:

lower: the lower bin values
upper: the upper bin values
freq: the number of observations in each bin

Parameter Conversion for Weibull Distribution

Description

The function to_k_lambda_weibull converts the mean and standard deviation to the shape and scale for the Weibull distributions.

Usage

to_k_lambda_weibull(mu, sd)
to_k_lambda_weibull(mu, sd)

Arguments

`mu`	a numeric vector representing the means of Weibull distributions
`sd`	a numeric vector representing the standard deviations of Weibull distributions. `mu` and `sd` should have the same length.

Details

The purpose of this function is to convert the parameterization of Weibull distribution in the form of mean and standard deviation to the form of shape and scale. It can be used for specifying the initial values for the EM algorithm when the first-hand initial values are in the form of mean and standard deviation from K-means clustering algorithm.

Value

a list of two items

`k`	a vector of the shapes of Weibull distributions
`lambda`	a vector of the scales of Weibull distributions

Examples

to_k_lambda_weibull(2, 1)
to_k_lambda_weibull(c(2, 5), c(1, 0.7))

to_k_lambda_weibull(2, 1)
to_k_lambda_weibull(c(2, 5), c(1, 0.7))

Parameter Conversion for Gamma Distribution

Description

The function to_mu_sd_gamma converts the shape and rate to the mean and standard deviation

Usage

to_mu_sd_gamma(alpha, lambda)
to_mu_sd_gamma(alpha, lambda)

Arguments

`alpha`	a numeric vector representing the shape of one or more than one gamma distributions
`lambda`	a numeric vector representing the rate of one or more than one gamma distributions. `alpha` and `lambda` should have the same length.

Details

The purpose of this function is to convert the parameterization of gamma distribution in the form of shape and rate to the form of mean and standard deviation.

Value

a list of two items

`mu`	a vector of the means of gamma distributions
`sd`	a vector of the standard deviations of gamma distributions

Examples

to_mu_sd_gamma(2, 1)
to_mu_sd_gamma(c(2, 4), c(1, 1))

to_mu_sd_gamma(2, 1)
to_mu_sd_gamma(c(2, 4), c(1, 1))

Parameter Conversion for Lognormal Distribution

Description

The function to_mu_sd_lnorm converts the logarithm mean and logarithm standard deviation to the mean and standard deviation

Usage

to_mu_sd_lnorm(mulog, sdlog)
to_mu_sd_lnorm(mulog, sdlog)

Arguments

`mulog`	a vector of logarithm means of lognormal distributions
`sdlog`	a vector of logarithm standard deviations of lognormal distributions

Details

The purpose of this function is to convert the parameterization of lognormal distribution in the form of logarithm mean and logarithm standard deviation to the form of mean and standard deviation.

Value

a list of two items

`mu`	a vector of the means of lognormal distributions
`sd`	a vector of the standard deviations of lognormal distributions

Examples

to_mu_sd_lnorm(2, 1)
to_mu_sd_lnorm(c(2, 4), c(1, 1))

to_mu_sd_lnorm(2, 1)
to_mu_sd_lnorm(c(2, 4), c(1, 1))

Parameter Conversion for Weibull Distribution

Description

The function to_mu_sd_weibull converts the parameters of shape and scale of weibull distributions to the parameters of the mean and standard deviation.

Usage

to_mu_sd_weibull(k, lambda)
to_mu_sd_weibull(k, lambda)

Arguments

`k`	a numeric vector representing the shape of a series of Weibull distributions
`lambda`	a numeric vector representing the scale of a series of Weibull distributions. `k` and `lambda` should have the same length.

Details

The purpose of this function is to convert the parameterization of Weibull distribution in the form of shape and scale to the form of mean and standard deviation.

Value

a list of two items

`mu`	a vector of the means of Weibull distributions
`sd`	a vector of the standard deviations of Weibull distributions

Examples

to_mu_sd_weibull(2, 1)
to_mu_sd_weibull(c(2, 4), c(1, 1))

to_mu_sd_weibull(2, 1)
to_mu_sd_weibull(c(2, 4), c(1, 1))

Parameter Conversion for Lognormal Distribution

Description

The function to_mulog_sdlog_lnorm converts the mean and standard deviation to the logarithm mean and logarithm standard deviation

Usage

to_mulog_sdlog_lnorm(mu, sd)
to_mulog_sdlog_lnorm(mu, sd)

Arguments

`mu`	a vector of means of lognormal distributions
`sd`	a vector of standard deviations of lognormal distributions

Details

The purpose of this function is to convert the parameterization of lognormal distribution in the form of mean and standard deviation to the form of logarithm mean and logarithm standard deviation. It can be used for specifying the initial values for the EM algorithm when the first-hand initial values are in the form of mean and standard deviation from K-means clustering algorithm.

Value

a list of two items

`mulog`	a vector of lognormal means of lognormal distributions
`sdlog`	a vector of lognormal standard deviations of lognormal distributions

Examples

to_mulog_sdlog_lnorm(2, 1)
to_mulog_sdlog_lnorm(c(2, 4), c(1, 1))

to_mulog_sdlog_lnorm(2, 1)
to_mulog_sdlog_lnorm(c(2, 4), c(1, 1))

Parameter Conversion for Gamma Distribution

Description

The function to_shape_rate_gamma converts the mean and standard deviation to the shape and rate

Usage

to_shape_rate_gamma(mu, sd)
to_shape_rate_gamma(mu, sd)

Arguments

`mu`	a numeric vector representing the means of gamma distributions
`sd`	a numeric vector representing the standard deviations of gamma distributions. `mu` and `sd` should have the same length.

Details

The purpose of this function is to convert the parameterization of gamma distribution in the form of mean and standard deviation to the form of shape and rate. It can be used for specifying the initial values for the EM algorithm when the first-hand initial values are in the form of mean and standard deviation from K-means clustering algorithm.

Value

a list of two items

`alpha`	a vector of the shapes of gamma distributions
`lambda`	a vector of the rates of gamma distributions

Examples

to_shape_rate_gamma(2, 1)
to_shape_rate_gamma(c(2, 4), c(1, 1))

to_shape_rate_gamma(2, 1)
to_shape_rate_gamma(c(2, 4), c(1, 1))

Package 'mixR'

Help Index

Finite Mixture Modeling for Raw and Binned Data

Description

Details

Author(s)

References

Binning the Raw Data

Description

Usage

Arguments

Details

Value

See Also

Examples

Bootstrap Likelihood Ratio Test for Finite Mixture Models

Description

Usage

Arguments

Details

Value

See Also

Examples

The Density of Finite Mixture Models

Description

Usage

Arguments

Details

Value

See Also

Examples

Initialization of the EM Algorithm

Description

Usage

Arguments

Details

Value

Examples

Finite Mixture Modeling for Raw Data and Binned Data

Description

Usage

Arguments

Details

Value

See Also

Examples

Plot Bootstrap Likelihood Ratio Test

Description

Usage

Arguments

Details

See Also

Examples

Plotting the Fitted Mixture Models

Description

Usage

Arguments

Details

See Also

Examples

Plot Method for Class selectEM

Description

Usage

Arguments

Details

See Also

Examples

Print Method for Class mixfitEM

Description

Usage

Arguments

Details

See Also

Examples

Print Method for Class selectEM

Description

Usage

Arguments

Details

See Also

Plot Method for Class `selectEM`

Print Method for Class `mixfitEM`

Print Method for Class `selectEM`